def run(args):
# Load the input data
if args.verbose: print('* Loading infmat and heat files...')
infmat = hnio.load_infmat(args.infmat_file, args.infmat_name)
full_index2gene = hnio.load_index(args.infmat_index_file)
using_json_heat = os.path.splitext(args.heat_file.lower())[1] == '.json'
if using_json_heat:
heat = json.load(open(args.heat_file))['heat']
else:
heat = hnio.load_heat_tsv(args.heat_file)
print("* Loaded heat scores for %s genes" % len(heat))
# filter out genes not in the network
heat = hnheat.filter_heat_to_network_genes(heat,
set(full_index2gene.values()))
# genes with score 0 cannot be in output components, but are eligible for heat in permutations
heat, addtl_genes = hnheat.filter_heat(
heat, None, False, 'There are ## genes with heat score 0')
if args.verbose: print('* Creating similarity matrix...')
sim, index2gene = hn.similarity_matrix(infmat, full_index2gene, heat, True)
# Create and output the dendrogram
createDendrogram(sim, list(index2gene.values()), args.output_directory,
vars(args), args.verbose)
def load_direct_heat(args):
heat = hnio.load_heat_tsv(args.heat_file)
#ensure that all heat scores are positive
bad_genes = [gene for gene in heat if heat[gene] < 0]
if bad_genes:
raise ValueError("ERROR: All gene heat scores must be non-negative. There are %s genes with\
negative heat scores: %s" % (len(bad_genes), bad_genes))
heat, excluded_genes, args.min_heat_score = hnheat.filter_heat(heat, args.min_heat_score)
return heat, excluded_genes
def load_direct_heat(args):
heat = hnio.load_heat_tsv(args.heat_file)
print "* Loading heat scores for %s genes" % len(heat)
#ensure that all heat scores are positive
bad_genes = [gene for gene in heat if heat[gene] < 0]
if bad_genes:
raise ValueError("ERROR: All gene heat scores must be non-negative. There are %s genes with\
negative heat scores: %s" % (len(bad_genes), bad_genes))
heat, _ = hnheat.filter_heat(heat, args.min_heat_score, True,
'Assigning score 0 to ## genes with score below %s' % args.min_heat_score)
return heat
def load_direct_heat(args):
heat = hnio.load_heat_tsv(args.heat_file)
print("* Loading heat scores for %s genes" % len(heat))
#ensure that all heat scores are positive
bad_genes = [gene for gene in heat if heat[gene] < 0]
if bad_genes:
raise ValueError(
"ERROR: All gene heat scores must be non-negative. There are %s genes with\
negative heat scores: %s" %
(len(bad_genes), bad_genes))
heat, _ = hnheat.filter_heat(
heat, args.min_heat_score, True,
'Assigning score 0 to ## genes with score below %s' %
args.min_heat_score)
return heat
def run(args): # Load the input data if args.verbose: print '* Loading infmat and heat files...' infmat = hnio.load_infmat(args.infmat_file, args.infmat_name) full_index2gene = hnio.load_index(args.infmat_index_file) using_json_heat = os.path.splitext(args.heat_file.lower())[1] == '.json' if using_json_heat: heat = json.load(open(args.heat_file))['heat'] else: heat = hnio.load_heat_tsv(args.heat_file) print "* Loaded heat scores for %s genes" % len(heat) # filter out genes not in the network heat = hnheat.filter_heat_to_network_genes(heat, set(full_index2gene.values())) # genes with score 0 cannot be in output components, but are eligible for heat in permutations heat, addtl_genes = hnheat.filter_heat(heat, None, False, 'There are ## genes with heat score 0') if args.verbose: print '* Creating similarity matrix...' sim, index2gene = hn.similarity_matrix(infmat, full_index2gene, heat, True) # Create and output the dendrogram createDendrogram( sim, index2gene.values(), args.output_directory, vars(args), args.verbose )
def run(args):
# create output directory if doesn't exist; warn if it exists and is not empty
if not os.path.exists(args.output_directory):
os.makedirs(args.output_directory)
if len(os.listdir(args.output_directory)) > 0:
print("WARNING: Output directory is not empty. Any conflicting files will be overwritten. "
"(Ctrl-c to cancel).")
infmat = scipy.io.loadmat(args.infmat_file)[INFMAT_NAME]
infmat_index = hnio.load_index(args.infmat_index_file)
heat = hnio.load_heat_tsv(args.heat_file)
# filter out genes with heat score less than min_heat_score
heat, addtl_genes, args.min_heat_score = hnheat.filter_heat(heat, args.min_heat_score)
# find smallest delta
deltas = ft.get_deltas_for_network(args.permuted_networks_path, heat, INFMAT_NAME,
infmat_index, MAX_CC_SIZES,
args.num_permutations, args.parallel)
# and run HotNet with the median delta for each size
run_deltas = [np.median(deltas[size]) for size in deltas]
M, gene_index = hn.induce_infmat(infmat, infmat_index, sorted(heat.keys()))
h = hn.heat_vec(heat, gene_index)
sim = hn.similarity_matrix(M, h)
# load interaction network edges and determine location of static HTML files for visualization
edges = hnio.load_ppi_edges(args.edge_file) if args.edge_file else None
index_file = '%s/viz_files/%s' % (hotnet2.__file__.rsplit('/', 1)[0], VIZ_INDEX)
subnetworks_file = '%s/viz_files/%s' % (hotnet2.__file__.rsplit('/', 1)[0], VIZ_SUBNETWORKS)
gene2index = dict([(gene, index) for index, gene in infmat_index.iteritems()])
for delta in run_deltas:
# create output directory
delta_out_dir = args.output_directory + "/delta_" + str(delta)
if not os.path.isdir(delta_out_dir):
os.mkdir(delta_out_dir)
# find connected components
G = hn.weighted_graph(sim, gene_index, delta)
ccs = hn.connected_components(G, args.min_cc_size)
# calculate significance (using all genes with heat scores)
print "* Performing permuted heat statistical significance..."
heat_permutations = p.permute_heat(heat, args.num_permutations, addtl_genes, args.parallel)
sizes = range(2, 11)
print "\t- Using no. of components >= k (k \\in",
print "[%s, %s]) as statistic" % (min(sizes), max(sizes))
sizes2counts = stats.calculate_permuted_cc_counts(infmat, infmat_index, heat_permutations,
delta, sizes, args.parallel)
real_counts = stats.num_components_min_size(G, sizes)
size2real_counts = dict(zip(sizes, real_counts))
sizes2stats = stats.compute_statistics(size2real_counts, sizes2counts, args.num_permutations)
# sort ccs list such that genes within components are sorted alphanumerically, and components
# are sorted first by length, then alphanumerically by name of the first gene in the component
ccs = [sorted(cc) for cc in ccs]
ccs.sort(key=lambda comp: comp[0])
ccs.sort(key=len, reverse=True)
# write output
heat_dict = {"heat": heat, "parameters": {"heat_file": args.heat_file}}
heat_out = open(os.path.abspath(delta_out_dir) + "/" + HEAT_JSON, 'w')
json.dump(heat_dict, heat_out, indent=4)
heat_out.close()
args.heat_file = os.path.abspath(delta_out_dir) + "/" + HEAT_JSON
args.delta = delta
output_dict = {"parameters": vars(args), "sizes": hn.component_sizes(ccs),
"components": ccs, "statistics": sizes2stats}
hnio.write_significance_as_tsv(os.path.abspath(delta_out_dir) + "/" + SIGNIFICANCE_TSV,
sizes2stats)
json_out = open(os.path.abspath(delta_out_dir) + "/" + JSON_OUTPUT, 'w')
json.dump(output_dict, json_out, indent=4)
json_out.close()
hnio.write_components_as_tsv(os.path.abspath(delta_out_dir) + "/" + COMPONENTS_TSV, ccs)
# write visualization output if edge file given
if args.edge_file:
viz_data = {"delta": delta, 'subnetworks': list()}
for cc in ccs:
viz_data['subnetworks'].append(viz.get_component_json(cc, heat, edges, gene2index,
args.network_name))
delta_viz_dir = '%s/viz/delta%s' % (args.output_directory, delta)
if not os.path.isdir(delta_viz_dir):
os.makedirs(delta_viz_dir)
viz_out = open('%s/subnetworks.json' % delta_viz_dir, 'w')
json.dump(viz_data, viz_out, indent=4)
viz_out.close()
shutil.copy(subnetworks_file, delta_viz_dir)
if args.edge_file:
viz.write_index_file(index_file, '%s/viz/%s' % (args.output_directory, VIZ_INDEX), run_deltas)